Sinogram-based data augmentation by extracting feature points in the Radon space

Identifying defects in metallic parts from additive manufacturing is crucial. It can be time-consuming and repeti-tive, prone to errors and high costs. In non-destructive testing, characterising defects in these parts is made possible through X-ray computed tomography. Most of the studies provide analysis in the reconstructed images which is computationally demanding, sensible to artefacts and reduces spatial resolution. In this paper, we introduce our iterative sinusoidal Hough Transform (iSHT) for feature extraction directly in Radon space based on our restrained spatial representation method. We designed a 2D numerical phantom to demonstrate the effectiveness of the methodology up to the augmentation of this dataset.